System and method for monitoring an industrial vehicle

ABSTRACT

A system and method for monitoring an industrial vehicle are presented. The system includes a first imaging subsystem for acquiring a plurality of load-carrying-portion images. A cargo-detection subsystem is configured for analyzing each of the plurality of load-carrying-portion images to determine whether cargo is positioned on the load-carrying portion. A power-detection subsystem is configured for determining when the industrial vehicle is running. A motion-detection subsystem is configured for determining when the industrial vehicle is in motion. An analytics subsystem is configured for calculating the amount of time that the industrial vehicle is running, the amount of time that the industrial vehicle is running while cargo is positioned on the load-carrying portion, the amount of time the industrial vehicle is in motion, and the amount of time the industrial vehicle is in motion while cargo is positioned on the load-carrying portion.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims the benefit of U.S. patent applicationSer. No. 14/969,748 for a System and Method for Monitoring an IndustrialVehicle filed on Dec. 15, 2015 (and published Jun. 30, 2016 as U.S.Patent Publication No. 2016/0189443), now U.S. Pat. No. 9,734,639, whichclaims the benefit of U.S. Patent Application No. 62/098,708 for aSystem and Method for Monitoring an Industrial Vehicle filed on Dec. 31,2014. Each of the foregoing patent applications, patent publication, andpatent is hereby incorporated by reference in its entirety.

FIELD OF THE INVENTION

The disclosure relates generally to industrial vehicles, and moreparticularly, to a system and method for monitoring an industrialvehicle.

BACKGROUND OF THE DISCLOSURE

Industrial vehicles are commonly used to transport materials andequipment in a facility. Industrial vehicles are particularly useful fortransporting loads that are too heavy to be transported by human-poweredmeans. Because a fleet of industrial vehicle can represent a significantinvestment for a business, tracking the use of these industrial vehiclesis important to maximizing their utility and their life-expectancy.

Warehouse operators, fleet supervisors, and other interested partiescommonly track the following metrics in regard to their industrialvehicles: hours of operation, hours of operation while carrying a load,hours in motion, hours in motion while carrying a load, and hours ofoperation with a driver inside the vehicle. Tracking these metrics canprovide an understanding of how each industrial vehicle is beingutilized. This can be important in anticipating maintenance needs aswell as the need to purchase additional industrial vehicles. Thisinformation may also be useful in providing training to employees tobetter maximize their use of an industrial vehicle.

One conventional approach to gathering these types of metrics would beto install sensors on the industrial vehicle. For example, a scalesensor could be installed to determine whether there is cargo present inthe load-carrying portion of the industrial vehicle. Another sensorcould be installed to detect when a driver is present in the drivercompartment. Installation of these types of sensors would require acostly retrofit to an existing fleet.

What is needed is a system for monitoring an industrial vehicle that canbe easily installed into an existing industrial vehicle, or that can beeasily integrated into the manufacturing of a new industrial vehicle.

SUMMARY

In one aspect, the present disclosure embraces a system for monitoringan industrial vehicle having a load-carrying portion and a drivercompartment. The system includes a first imaging subsystem for acquiringa plurality of load-carrying-portion images. The system also includes acargo-detection subsystem. The cargo-detection subsystem is incommunication with the first imaging subsystem. The cargo-detectionsubsystem is configured for analyzing each the plurality ofload-carrying-portion images to determine whether cargo is positioned onthe load-carrying portion. The system also includes a power-detectionsubsystem for determining when the industrial vehicle is running. Thesystem also includes a motion-detection subsystem for determining whenthe industrial vehicle is in motion. The system also includes ananalytics subsystem. The analytics subsystem is in communication withthe cargo-detection subsystem, the power-detection subsystem, and themotion-detection subsystem. The analytics subsystem is configured forcalculating (i) the amount of time that the industrial vehicle isrunning; (ii) the amount of time that the industrial vehicle is runningwhile cargo is positioned on the load-carrying portion; (iii) the amountof time the industrial vehicle is in motion; and (iv) the amount of timethe industrial vehicle is in motion while cargo is positioned on theload-carrying portion.

In an alternative embodiment, the cargo-detection subsystem determineswhether cargo is positioned on the load-carrying portion by comparingeach of the plurality of load-carrying-portion images to a configurationimage of the load-carrying portion.

In another alternative embodiment, the configuration image of theload-carrying portion is an image of the load-carrying portion with nocargo.

In yet another alternative embodiment, the system includes a secondimaging subsystem for acquiring a plurality of driver compartmentimages. The system also includes a driver-detection subsystem foranalyzing the plurality of driver compartment images to determinewhether a driver is present in the driver compartment. According to thisalternative embodiment of the system according to the presentdisclosure, the analytics subsystem is in communication with thedriver-detection subsystem and is further configured for calculating thetotal time that the vehicle is running while a driver is present in thedriver compartment.

In yet another alternative embodiment, the driver-detection subsystemdetermines whether a driver is present in the driver compartment bycomparing each of the plurality of driver compartment images to aconfiguration image of the driver compartment.

In yet another alternative embodiment, the configuration image of thedriver compartment is an image of the driver compartment with no driverpresent.

In yet another alternative embodiment, the driver-detection subsystemincludes a facial recognition module. According to this alternativeembodiment of the system according to the present disclosure, thedriver-detection subsystem determines that a driver is present in thedriver compartment if the facial recognition module detects a human facein an image of the driver compartment.

In another aspect, the present disclosure embraces a self-monitoringindustrial vehicle having a load-carrying portion and a drivercompartment. The industrial vehicle includes a first imaging subsystemfor acquiring a plurality of load-carrying-portion images. Theindustrial vehicle also includes a cargo-detection subsystem incommunication with the first imaging subsystem. The cargo-detectionsubsystem is configured for analyzing the plurality ofload-carrying-portion images to determine whether cargo is positioned onthe load-carrying portion. The industrial vehicle also includes apower-detection subsystem for determining when the industrial vehicle isrunning. The industrial vehicle also includes a motion-detectionsubsystem for determining when the industrial vehicle is in motion. Theindustrial vehicle also includes an analytics subsystem in communicationwith the cargo-detection subsystem, the power-detection subsystem, andthe motion-detection subsystem. The analytics subsystem is configuredfor calculating (i) the amount of time that the industrial vehicle isrunning; (ii) the amount of time that the industrial vehicle is runningwhile cargo is positioned on the load-carrying portion; (iii) the amountof time the industrial vehicle is in motion; and (iv) the amount of timethe industrial vehicle is in motion while cargo is positioned on theload-carrying portion.

In another aspect, the present disclosure embraces a computerized methodof monitoring an industrial vehicle having a load-carrying portion and adriver compartment. According to the computerized method, a plurality ofload-carrying-portion images of the industrial vehicle are acquired. Theplurality of load-carrying-portion images of the industrial vehicle areanalyzed using a computer to determine whether cargo is positioned onthe load-carrying portion. It is determined when the industrial vehicleis running and when the industrial vehicle is in motion. The followingare calculated using the computer: (i) the amount of time that theindustrial vehicle is running; (ii) the amount of time that theindustrial vehicle is running while cargo is positioned on theload-carrying portion; (iii) the amount of time the industrial vehicleis in motion; and (iv) the amount of time the industrial vehicle is inmotion while cargo is positioned on the load-carrying portion.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a perspective view of an exemplary industrial vehicle.

FIG. 2 is block diagram of an exemplary embodiment of a system accordingto the present disclosure.

FIG. 3 is block diagram of an alternative embodiment of a systemaccording to the present disclosure.

FIG. 4 is block diagram of an alternative embodiment of a systemaccording to the present disclosure.

FIG. 5 is perspective view of a self-monitoring industrial vehicleaccording to the present disclosure.

FIG. 6 is a perspective view of an alternative embodiment of aself-monitoring industrial vehicle according to the present disclosure.

DETAILED DESCRIPTION

The system according to the present invention is directed towardmonitoring an industrial vehicle having a load-carrying portion and adriver compartment. Industrial vehicles include, for example, forklifts,pallet trucks, material handling vehicles, tow tractors, lift trucks,container handlers, orderpickers, sideloaders, stacker/retrievalmachines, reach trucks, swing reach trucks, counterbalanced forkliftvehicles, and similar vehicles driven (e.g., maneuvered, operated, etc.)by a human operator. Typically, these industrial vehicles are operatedin a facility that includes a warehouse. Other types of facilitiesinclude ports, rail yards, trucking terminals, distribution centers,etc.

Rather than monitoring the industrial vehicle exclusively through theinstallation of, or interconnection with, sensors, the present systemmonitors the use of the industrial vehicle, at least in part, throughthe analysis of images of the industrial vehicle. For example, todetermine whether the industrial vehicle is carrying a load (e.g.,carrying cargo), the system acquires load-carrying-portion images (e.g.,the forks of a forklift) of the industrial vehicle. In other words, thesystem acquires an image (e.g., a digital photograph, digital image,etc.) depicting that portion of the industrial vehicle which typicallycarries a load (e.g., cargo). The system applies image processingtechniques to determine, based upon the acquired images, whether cargois positioned on the load-carrying portion. The system may be configuredby using a configuration image, such as an image of the load-carryingportion with no cargo present. If an acquired image substantiallymatches the configuration image, the system determines that there is nocargo present. If, however, an acquired image substantially differs fromthe configuration image (e.g., some or all of the load-carrying portionis obscured (e.g., obscured by an object)), the system determines thatcargo is present.

The system applies a similar approach to determine whether an operatoris present in the driver portion. The system acquires driver compartmentimages. Applying image processing techniques, the system determineswhether a driver is present in the driver compartment. The system mayincorporate as part of the applied image processing techniques a facialrecognition algorithm for recognizing the presence of a human face in animage. The facial recognition technique may even be used to determinethe specific identify of the operator, which may be used to log operatortime, for example.

The system according to the present disclosure advantageously canleverage existing components of an industrial vehicle. Some industrialvehicles are equipped with at least one camera. This camera may be usedto record and preserve video of the industrial vehicle's movements inthe moments before (e.g., the 30 seconds preceding) an accident. Theremay be more than one camera (e.g., for recording the area in front ofand behind the industrial vehicle). These cameras are typically incommunication with a vehicle mount computer (or other onboard computerdevice), which controls the recording and preserving of video (e.g., bystoring the video on a hard disk). The system according to the presentdisclosure can readily be integrated with this type of equipment,potentially through the installation of appropriate software on thevehicle mount computer. In this way, the system according to the presentdisclosure provides a cost-efficient solution for monitoring theoperation of an industrial vehicle.

Referring to the figures in the accompanying drawings, certainillustrative embodiments of the system according to the presentdisclosure will be described in great detail, where like elements willbe indicated using like reference numerals. Turning now to the drawings,FIG. 1 is an exemplary embodiment of an industrial vehicle 105 suitablefor monitoring by the system according to the present disclosure. Inthis instance, the industrial vehicle is a forklift. As mentioned above,however, a suitable industrial vehicle may be any of a variety ofindustrial vehicles.

The industrial vehicle 105 has a load-carrying portion 110 (e.g., acargo portion, a storage section, etc.). In this instance, theload-carrying portion 110 includes the forks of the forklift. Theload-carrying portion 110 may also include the heel and the mastassembly. In instances where the industrial vehicle is not a forklift,the load-carrying portion may include a truck bed (e.g., cargo bed), atowing assembly, a platform, or any other portion of the industrialvehicle configured for carrying cargo 120. The cargo 120 may be any typeof load being carried (e.g., transported) by the industrial vehicle,including pallets and objects positioned on a pallet, containers,packages, goods, equipment, and the like.

The industrial vehicle 105 also has a driver compartment 125. Theoperator (e.g., driver) of the industrial vehicle is positioned at thedriver compartment when the operator is driving

(e.g., operating) the industrial vehicle. The exemplary industrialvehicle 105 depicted in FIG. 1 features a driver compartment 125 at therear portion of the industrial vehicle 105 where the operator standswhile driving the industrial vehicle 105. The driver compartment 125 maybe in a variety of configurations. One common configuration of thedriver compartment includes a seat upon which the driver sits duringoperation of the industrial vehicle 105. In other configurations, theoperator of the industrial vehicle 105 may not be positioned on orinside the industrial vehicle 125 while operating the industrial vehicle125. For example, some industrial vehicles 105 may be operated by adriver who is walking in front of, behind, or beside the industrialvehicle 105. In these configurations, the driver compartment typicallyincludes the portion of the industrial vehicle 105 having the controlsto the industrial vehicle 105 (e.g., steering controls). In instanceswhere the industrial vehicle is controlled remotely, the drivercompartment is the location where the driver is positioned whileoperating the vehicle.

As shown in FIG. 2, the system 100 for monitoring an industrial vehicle105 according to the present disclosure includes a first imagingsubsystem 130. The first imaging subsystem 130 is configured foracquiring a plurality of load-carrying-portion images. Typically, thefirst imaging subsystem 130 includes a video camera (e.g., CCD, CMOS,etc.) and the plurality of images is a video (e.g., digital video,digital video file, etc.). Video is usually captured as digital video.Less commonly, the video may be analog video. Alternatively, the firstimaging system 130 may include a still camera (e.g., digital SLR camera)for acquiring images in the form of still images. Typically, the firstimaging subsystem 130 includes means for storing the acquired images,such as a memory (e.g., digital memory, hard drive, solid-state harddrive, etc.).

As mentioned, the first imaging subsystem 130 is configured foracquiring a plurality of images of the load carrying-portion 135 of theindustrial vehicle. Typically, configuring the first imaging subsystem130 entails positioning a camera such that the camera's field of viewencompasses at least the load-carrying portion 110.

The system 100 also includes a cargo-detection subsystem 140. Thecargo-detection subsystem 140 is in communication (e.g., electroniccommunication, electronically connected, etc.) with the first imagingsubsystem 130 such that information, including load-carrying-portionimages 135 can be transmitted from the first imaging subsystem 130 tothe cargo-detection subsystem 140.

The cargo-detection subsystem 140 is configured for analyzing each ofthe plurality of load-carrying-portion images 135 (e.g., analyzingsubstantially all of the frames of video) to determine whether cargo 120is positioned on the load-carrying portion 110. Typically, thecargo-detection subsystem 140 includes computer software stored innon-transitory computer readable memory and executed by a processor(e.g., computer processor, CPU). The computer software usually includesimage processing algorithms commonly used to analyze digital photographsand other digital images. The processor and non-transitory computerreadable medium may be components of a vehicle mount computer mountedto, or installed in, the industrial vehicle 105. By analyzing (e.g.,processing) the load-carrying-portion images 135 (e.g., using imageprocessing software), the cargo-detection subsystem is able to determinein which images cargo 120 is present on the load-carrying portion 110and in which images cargo 120 is not present.

In one embodiment, the cargo-detection subsystem 140 utilizes aconfiguration image of the load-carrying portion to determine whethercargo is present. Typically, the configuration image is an image of theload-carrying portion 110 as it appears when no cargo 120 is present(e.g., when the industrial vehicle is not under load). Thecargo-detection subsystem 140 compares the load-carrying-portion images135 received from the first imaging subsystem 130 to this configurationimage. If, for example, the load-carrying portion 110 is obscured in agiven image, then the cargo-detection subsystem 140 may assume thatcargo 120 is obstructing the view and, therefore, determine that theindustrial vehicle 105 is under load (e.g., that the industrial vehicleis carrying cargo).

The system 100 also includes a power-detection subsystem 150. The powerdetection subsystem 150 is configured to determine when the industrialvehicle is running (e.g., powered up, under power, turned on, etc.). Thepower-detection subsystem 150 may determine when the industrial vehicleis running using a variety of suitable techniques. For example, thepower-detection subsystem 150 may receive a signal (e.g., electricalsignal) from the industrial vehicle's ignition system, engine, and/ormotor. Alternatively, the power-detection subsystem 150 may determinethat when the system 100 is powered, then the industrial vehicle 105 isalso running.

The system 100 also includes a motion-detection subsystem 155. Themotion-detection subsystem 155 is configured for determining when theindustrial vehicle 105 is in motion (e.g., when it is moving, beingdriven forward/backward, etc.). The motion-detection subsystem 155 maydetermine that the industrial vehicle 105 is in motion using a varietyof techniques. For example, and without intending to limit thedisclosure, the motion-detection subsystem 155 may include, or may beconfigured to receive a signal from, an accelerometer. Alternatively,the motion-detection subsystem 155 may analyze the load-carrying-portionimages 135 to determine when the industrial vehicle 105 is in motion.This may be done by processing the images to determine when objects inthe background (e.g., walls, floors, etc.) are moving in relation to theindustrial vehicle 105.

The system 100 also includes an analytics subsystem 160. The analyticssubsystem 160 is in communication with (e.g., electronically connectedto) the cargo-detection subsystem 140, the power-detection subsystem150, and the motion-detection subsystem 155. As such, the analyticssubsystem 160 can receive information regarding whether the industrialvehicle is under load (e.g., carrying cargo), whether the industrialvehicle is under power, and whether the industrial vehicle is in motion.Typically, the analytics subsystem 160 is, or includes, computersoftware having algorithms for managing and manipulating the informationreceived by the analytics subsystem 160, including by generating variousmetrics for measuring, for example, the performance and utility of theindustrial vehicle. As will be appreciated by a person of ordinary skillin the art, the analytics subsystem 160 may also be, or include,computer hardware (e.g., integrated circuits) adapted to execute thesealgorithms.

In any event, the analytics subsystem 160 is configured for calculatingthe amount of time that the industrial vehicle is running. Typically,for each period of time that the power-detection subsystem 150 indicatesthat the industrial vehicle is running, the analytics subsystem 160increases a corresponding counter (e.g., time counter) by the sameperiod of time.

The analytics subsystem 160 is also configured for calculating theamount of time that the industrial vehicle is running while cargo 140 ispositioned on the load-carrying portion 110. Typically, for each periodof time that the power-detection subsystem 150 determines that theindustrial vehicle is under power at the same time that thecargo-detection subsystem 140 determines that the industrial vehicle 105is under load, the analytics subsystem 160 increases the correspondingcounter by the same period of time.

The analytics subsystem 160 is also configured to calculate the amountof time the industrial vehicle 105 is in motion. Typically, for eachperiod of time that the motion-detection subsystem 155 determines thatthe industrial vehicle 105 is in motion, the analytics subsystem 160increases the corresponding counter by the same period of time.

The analytics subsystem 160 is also configured to calculate the amountof time that the industrial vehicle 105 is in motion while cargo 120 ispositioned on the load-carrying portion 110. Typically, the analyticssubsystem 160 increases the appropriate counter by an amount of timeequal to the period of time when the motion-detection subsystem 155determines that the industrial vehicle 105 is in motion at the same timethat the cargo-detection subsystem 140 determines that there is cargo120 positioned on the load-carrying portion 110.

In an alternative embodiment illustrated in FIG. 3, the system 100according to the present disclosure includes a second imaging subsystem165. The second imaging subsystem 165 is configured for acquiring aplurality of driver compartment images 170. Driver compartment images170 are images (e.g., digital video) of the industrial vehicle's drivercompartment 125. Typically, the second imaging subsystem 165 includes acamera (e.g., video camera) positioned so that the driver compartment125 is within the camera's field of view.

A driver-detection subsystem 175 is configured for analyzing theplurality of driver compartment images to determine whether a driver ispresent in the driver compartment. Typically, the driver-detectionsubsystem 175 includes computer software that, when executed by acomputer processor, applies image processing techniques to analyze thedriver compartment images 170 (e.g., to identify objects depicted in theimages). The driver-detection subsystem 175 may utilize a configurationimage of the driver compartment 125. For example, the driver-detectionsubsystem 175 may compare a configuration image known to depict a drivercompartment with no driver to the received driver compartment images170. When a given driver compartment image 170 substantially differsfrom the configuration image, the driver-detection subsystem 175determines that a driver is present in the driver compartment 125.

According to this alternative embodiment, the analytics subsystem 160 isin communication with the driver-detection subsystem 175. The analyticssubsystem 160 can, therefore, receive information from thedriver-detection subsystem 175 regarding whether a driver is present inthe driver compartment 125 of the industrial vehicle 105. The analyticssubsystem 160 is configured for calculating the total time that thevehicle is running while a driver is present in the driver compartment.For example, the analytics subsystem 160 may increase the appropriatecounter (e.g., time counter) for a period of time equal to the durationthat the driver-detection subsystem 175 indicates a driver is presentwhile the power-detection subsystem 150 indicates that the industrialvehicle 105 is running.

In an alternative embodiment illustrated in FIG. 4, the driver-detectionsubsystem 175 includes a facial recognition module 185. Typically, thefacial recognition module 185 is software code that, when executed by acomputer processor, analyzes an image (e.g., an image computer file) todetect the presence of a human face (or other portion of a human body)in the image. The driver-detection subsystem 175 determines that adriver is present in the driver compartment if the facial recognitionmodule 185 detects a human face in a driver compartment image 170.

As depicted in FIGS. 5 and 6, the present disclosure also embraces aself-monitoring industrial vehicle 105. The industrial vehicle 105according to the present disclosure is outfitted with the components ofembodiments of the system 100 described herein, including a firstimaging subsystem 130, a cargo-detection subsystem 140, apower-detection subsystem 150, a motion-detection subsystem 155, and ananalytics subsystem 160. In the embodiment of the industrial vehicle 105illustrated in FIGS. 5 and 6, certain components

(e.g., those that may be embodied as computer software/hardware) areembodied in a vehicle mount computer positioned on the industrialvehicle 105. FIG. 6 illustrates an alternative embodiment of anindustrial vehicle 105 according to the present disclosure featuring asecond imaging subsystem 165 and a driver-detection subsystem 175.

The present disclosure also embraces a computerized method of monitoringan industrial vehicle having a load-carrying portion and a drivercompartment. Typically, the computerized method is carried out inaccordance with the functionality of the embodiments of the system ofthe present disclosure.

To supplement the present disclosure, this application incorporatesentirely by reference the following patents, patent applicationpublications, and patent applications:

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In the specification and figures, typical embodiments of the inventionhave been disclosed. The present invention is not limited to suchexemplary embodiments. Unless otherwise noted, specific terms have beenused in a generic and descriptive sense and not for purposes oflimitation.

What is claimed is:
 1. A system, comprising: a first imaging subsystemfor acquiring a plurality of load-carrying-portion images; acargo-detection subsystem in communication with the first imagingsubsystem, the cargo-detection subsystem configured for analyzing eachof the plurality of load-carrying-portion images to determine whethercargo is positioned on a load-carrying portion of an industrial vehicle,the industrial vehicle comprising a driver compartment; apower-detection subsystem for determining when the industrial vehicle isrunning; a motion-detection subsystem for determining when theindustrial vehicle is in motion; and an analytics subsystem incommunication with the cargo-detection subsystem, the power-detectionsubsystem, and the motion-detection subsystem, the analytics subsystemconfigured for calculating at least one of (i) the amount of time thatthe industrial vehicle is running; (ii) the amount of time that theindustrial vehicle is running while cargo is positioned on theload-carrying portion; (iii) the amount of time the industrial vehicleis in motion; and (iv) the amount of time the industrial vehicle is inmotion while cargo is positioned on the load-carrying portion.
 2. Thesystem of claim 1, wherein the cargo-detection subsystem determineswhether cargo is positioned on the load-carrying portion by comparingeach of the plurality of load-carrying-portion images to a configurationimage of the load-carrying portion.
 3. The system of claim 2, whereinthe configuration image of the load-carrying portion is an image of theload-carrying portion with no cargo.
 4. The system of claim 1,comprising: a second imaging subsystem for acquiring a plurality ofdriver compartment images; a driver-detection subsystem for analyzingthe plurality of driver compartment images to determine whether a driveris present in the driver compartment; wherein the analytics subsystem isin communication with the driver-detection subsystem and is furtherconfigured for calculating the total time that the vehicle is runningwhile a driver is present in the driver compartment.
 5. The system ofclaim 4, wherein the driver-detection subsystem determines whether adriver is present in the driver compartment by comparing each of theplurality of driver compartment images to a configuration image of thedriver compartment.
 6. The system of claim 5, wherein the configurationimage of the driver compartment is an image of the driver compartmentwith no driver present.
 7. The system of claim 4, wherein: thedriver-detection subsystem includes a facial recognition module; and thedriver-detection subsystem determines that a driver is present in thedriver compartment if the facial recognition module detects a human facein a driver compartment image.
 8. An industrial vehicle, comprising: aload-carrying portion; a driver compartment; a first imaging subsystemfor acquiring a plurality of load-carrying-portion images; acargo-detection subsystem in communication with the first imagingsubsystem, the cargo-detection subsystem configured for analyzing theplurality of load-carrying-portion images to determine whether cargo ispositioned on the load-carrying portion; a power-detection subsystem fordetermining when the industrial vehicle is running; a motion-detectionsubsystem for determining when the industrial vehicle is in motion; andan analytics subsystem in communication with the cargo-detectionsubsystem, the power-detection subsystem, and the motion-detectionsubsystem, the analytics subsystem configured for calculating at leastone of (i) the amount of time that the industrial vehicle is running;(ii) the amount of time that the industrial vehicle is running whilecargo is positioned on the load-carrying portion; (iii) the amount oftime the industrial vehicle is in motion; and (iv) the amount of timethe industrial vehicle is in motion while cargo is positioned on theload-carrying portion.
 9. The industrial vehicle of claim 8, wherein thecargo-detection subsystem determines whether cargo is positioned on theload-carrying portion by comparing each of the plurality ofload-carrying-portion images to a configuration image of theload-carrying portion.
 10. The industrial vehicle of claim 9, whereinthe configuration image of the load-carrying portion is an image of theload-carrying portion with no cargo.
 11. The industrial vehicle of claim8, comprising: a second imaging subsystem for acquiring a plurality ofdriver compartment images; a driver-detection subsystem in communicationwith the second imaging subsystem, the driver-detection subsystemconfigured for analyzing the plurality of driver compartment images todetermine whether a driver is present in the driver compartment; whereinthe analytics subsystem is in communication with the driver-detectionsubsystem and is further configured for calculating the total time thatthe vehicle is running while a driver is present in the drivercompartment.
 12. The industrial vehicle of claim 11, wherein thedriver-detection subsystem determines whether a driver is present in thedriver compartment by comparing each of the plurality of drivercompartment images to a configuration image of the driver compartment.13. The industrial vehicle of claim 12, wherein the configuration imageof the driver compartment is an image of the driver compartment with nodriver present.
 14. The industrial vehicle of claim 11, wherein: thedriver-detection subsystem includes a facial recognition module; and thedriver-detection subsystem determines that a driver is present in thedriver compartment if the facial recognition module detects a human facein a driver compartment image.
 15. A method, comprising: acquiring aplurality of load-carrying-portion images of an industrial vehicle;analyzing using a computer the plurality of load-carrying-portion imagesof the industrial vehicle to determine whether cargo is positioned on aload-carrying portion of the industrial vehicle; determining when theindustrial vehicle is running; determining when the industrial vehicleis in motion; calculating using the computer at least one of (i) theamount of time that the industrial vehicle is running; (ii) the amountof time that the industrial vehicle is running while cargo is positionedon the load-carrying portion; (iii) the amount of time the industrialvehicle is in motion; and (iv) the amount of time the industrial vehicleis in motion while cargo is positioned on the load-carrying portion. 16.The method of claim 15, wherein determining whether cargo is positioningin the load-carrying portion includes comparing each of the plurality ofload-carrying-portion images to a configuration image of theload-carrying portion.
 17. The method of claim 16, wherein theconfiguration image of the load-carrying portion is an image of theload-carrying portion with no cargo.
 18. The method of claim 15,comprising: acquiring a plurality of driver compartment images;analyzing using the computer the plurality of driver compartment imagesto determine whether a driver is present in a driver compartment of theindustrial vehicle; and calculating using the computer the total timethat the vehicle is running while a driver is present in the drivercompartment.
 19. The method of claim 18, wherein determining of whethera driver is present in the driver compartment includes comparing each ofthe plurality of driver compartment images to a configuration image ofthe driver compartment.
 20. The method of claim 18, wherein determiningwhether a driver is present in the driver compartment includes analyzingeach of the plurality of driver compartment images to determine whethera human face is present.